1 citations · 2 across the 3 of their papers we have counts for
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Half-Space Proximal Stochastic Gradient Method for Group-Sparsity Regularized Problem
Tianyi Chen, Guanyi Wang, Tianyu Ding +3
Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e.g., feature selection, compressed sensing and model compress…
Orthant Based Proximal Stochastic Gradient Method for -Regularized Optimization
Tianyi Chen, Tianyu Ding, Bo Ji +6
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel st…
An Approximation Algorithm for training One-Node ReLU Neural Network
Santanu S. Dey, Guanyi Wang, Yao Xie
Training a one-node neural network with ReLU activation function (One-Node-ReLU) is a fundamental optimization problem in deep learning. In this paper, we begin with proving the NP…
Sparse principal component analysis and its -relaxation
Santanu S. Dey, Rahul Mazumder, Marco Molinaro +1
Principal component analysis (PCA) is one of the most widely used dimensionality reduction methods in scientific data analysis. In many applications, for additional interpretabilit…
Mixed-integer linear representability, disjunctions, and Chvatal functions --- modeling implications
Amitabh Basu, Kipp Martin, Christopher Thomas Ryan +1
Jeroslow and Lowe gave an exact geometric characterization of subsets of that are projections of mixed-integer linear sets, also known as MILP-representable or MILP-…
The Strength of Multi-row Aggregation Cuts for Sign-pattern Integer Programs
Santanu S. Dey, Andres Iroume, Guanyi Wang
In this paper, we study the strength of aggregation cuts for sign-pattern integer programs (IPs). Sign-pattern IPs are a generalization of packing IPs and are of the form $\{x\in \…